mlsanityData to 14 Oct 2026

Digital Twin-based Control Co-Design of Full Vehicle Active Suspensions via Deep Reinforcement Learning

arXiv 2512.03891 ↗First posted 3 Dec 2025cs.RO cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Active suspension systems are critical for enhancing vehicle comfort, safety, and stability, yet their performance is often limited by fixed hardware designs and control strategies that cannot adapt to uncertain and dynamic operating conditions. Recent advances in digital twins (DTs) and deep reinforcement learning (DRL) offer new opportunities for real-time, data-driven optimization across a vehicle's lifecycle. However, integrating these technologies into a unified framework remains an open challenge. This work presents a DT-based control co-design (CCD) framework for full-vehicle active suspensions using multi-generation design concepts. By integrating automatic differentiation into DRL, we jointly optimize physical suspension components and control policies under varying driver behaviors and environmental uncertainties. DRL also addresses the challenge of partial observability, where only limited states can be sensed and fed back to the controller, by learning optimal control actions directly from available sensor information. The framework incorporates model updating with quantile learning to capture data uncertainty, enabling real-time decision-making and adaptive learning from digital-physical interactions. The approach demonstrates personalized optimization of suspension systems under two distinct driving settings (mild and aggressive). Results show that the optimized systems achieve smoother trajectories and reduce control efforts by approximately 43% and 52% for mild and aggressive, respectively, while maintaining ride comfort and stability. Contributions include: developing a DT-enabled CCD framework integrating DRL and uncertainty-aware model updating for full-vehicle active suspensions, introducing a multi-generation design strategy for self-improving systems, and demonstrating personalized optimization of active suspension systems for distinct driver types.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 84 references that are in the map. First posted on arXiv 3 Dec 2025.

Builds on

20 of 84 references.

References · 20

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8 more
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  1. 2025Digital Twin-enabled Multi-generation Control Co-Design with Deep Reinforcement Learning
  2. 2024Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control
  3. 2022Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin
  4. 2024Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Models
  5. 2024Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
  6. 2025Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving
  7. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  8. 2025Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits